Smoothing the Top-k Exposure Boundary for Sparse Mixture-of-Experts
Sparse Mixture-of-Experts models scale parameter capacity efficiently while maintaining a fixed compute budget per token. However, traditional training paradigms enforce a static choice of top-$k$ experts, which converts a continuous routing distribution into a rigid step function. This constraint introduces a brittle boundary where highly competitive experts are arbitrarily separated into full-supervision and zero-feedback zones based on minor score fluctuations. To address this issue, we propose Elastic Expert Routing, which stochastically samples the active expert budget from a localized discrete distribution centered at $k$. Over multiple training iterations, this mechanism softens th...
| Authors | Yunkai Chai, Tong Zhu, Xiaoye Qu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 October 2026 |
| Last updated at source | 12 October 2026 |
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